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Related Experiment Video

Updated: May 17, 2026

Application of a Dual Upper Limb Task-Oriented Robotic System for the Functional Recovery of the Upper Limb in Stroke Patients
05:28

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Published on: October 11, 2024

Upper and Lower-Limb Motor Decoding for Adaptive and Generalized Neural Rehabilitation.

Hunmin Lee, Daehee Seo

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 15, 2026
    PubMed
    Summary

    This study introduces a novel framework for decoding motor intentions from electromyographic (EMG) signals, improving accuracy across different limbs and individuals. The system enables rapid adaptation with minimal data, advancing neurorehabilitation and assistive technologies.

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    Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Machine Learning

    Background:

    • Electromyographic (EMG) signal decoding is crucial for neurorehabilitation and assistive devices.
    • Current methods face limitations in limb specificity, generalization, and data requirements.

    Purpose of the Study:

    • To develop a unified, cross-limb framework for motor intention decoding.
    • To overcome limitations of existing EMG decoding approaches, enhancing generalization and reducing data dependency.

    Main Methods:

    • A principled feature selection identifies domain-invariant EMG representations.
    • Reptile-based meta-learning with self-supervised pseudo-labeling enables few-shot adaptation.
    • Bias-aware sampling stabilizes adaptation in low-data scenarios.

    Main Results:

    • Achieved state-of-the-art decoding accuracy across upper and lower limbs.
    • Demonstrated superior performance in inter-session, inter-subject, and inter-dataset conditions.
    • Framework shows adaptability without repeated recalibration.

    Conclusions:

    • The proposed framework offers a scalable and clinically viable solution for motor intention decoding.
    • It significantly advances the potential of neurorehabilitation and assistive technologies.
    • The approach reduces the need for extensive labeled data, facilitating real-world application.